import streamlit as st
import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta
import plotly.graph_objects as go

# ---------------------------
# Configuration and Constants
# ---------------------------

# Define Indian sectors and their corresponding prominent stocks
SECTORS = {
    'Information Technology': ['TCS.NS', 'INFY.NS', 'WIPRO.NS', 'HCLTECH.NS', 'TECHM.NS'],
    'Pharmaceuticals': ['SUNPHARMA.NS', 'DRREDDY.NS', 'CIPLA.NS', 'BIOCON.NS', 'DIVISLAB.NS'],
    'Banking': ['HDFCBANK.NS', 'ICICIBANK.NS', 'KOTAKBANK.NS', 'AXISBANK.NS', 'SBIN.NS'],
    'Energy': ['RELIANCE.NS', 'ONGC.NS', 'IOC.NS', 'BPCL.NS', 'TATAMOTORS.NS'],
    'Automobiles': ['MARUTI.NS', 'TATAMOTORS.NS', 'BAJAJ-AUTO.NS', 'M&M.NS', 'HEROMOTOCO.NS'],
    'Consumer Goods': ['HINDUNILVR.NS', 'ITC.NS', 'NESTLEIND.NS', 'TATACONSUM.NS', 'DABUR.NS'],
    'Financial Services': ['BAJAJFINSV.NS', 'HDFC.NS', 'AXISBANK.NS', 'ICICIPRULI.NS', 'INDUSINDBK.NS'],
    'Metals': ['TATASTEEL.NS', 'JSWSTEEL.NS', 'HINDALCO.NS', 'SAIL.NS', 'JINDALSTEL.NS'],
    'Telecommunication Services': ['BHARTIARTL.NS', 'RELIANCE.NS', 'TATACOMM.NS', 'VODAFONE-I.NS', 'IDEA.NS'],
    'Real Estate': ['DLF.NS', 'PROPTIA.NS', 'OIL.NS', 'BRIGADE.NS', 'ANSALHSG.NS']
}

COLORS = [
    '#2E7D32', '#1976D2', '#C62828', '#F57C00', 
    '#6A1B9A', '#283593', '#00838F', '#558B2F',
    '#D84315', '#4527A0', '#00695C'
]

# ---------------------------
# Utility Functions
# ---------------------------

def get_high_performing_stocks(sector, top_n=5, period='1mo'):
    """
    Fetches and returns the top N high performing stocks in the specified sector over a given period.
    
    Parameters:
        sector (str): The sector name.
        top_n (int): Number of top performers to return.
        period (str): Time period for performance calculation (e.g., '1mo').
    
    Returns:
        pd.DataFrame: DataFrame containing top N performing stocks.
    """
    if sector not in SECTORS:
        st.error(f"No data available for sector: {sector}")
        return pd.DataFrame()
    
    tickers = SECTORS[sector]
    data = {}
    company_names = []
    for ticker in tickers:
        stock = yf.Ticker(ticker)
        hist = stock.history(period=period)
        if not hist.empty and len(hist['Close']) >= 2:
            hist = hist.sort_index()
            latest_close = hist['Close'][-1]
            oldest_close = hist['Close'][0]
            performance = ((latest_close - oldest_close) / oldest_close) * 100
            data[ticker] = performance
            info = stock.info
            company_names.append(info.get('shortName', 'N/A'))
        else:
            data[ticker] = 0.0  # Assign 0.0% if insufficient data
            company_names.append('N/A')
    
    # Create DataFrame
    df = pd.DataFrame(list(data.items()), columns=['Ticker', 'Performance (%)'])
    df['Company'] = company_names
    # Sort by performance descending
    df = df.sort_values(by='Performance (%)', ascending=False)
    # Return top N
    return df.head(top_n)

# ---------------------------
# Streamlit UI
# ---------------------------

def main():
    st.title("📈 High Performing Shares by Sector")
    
    # Sector selection
    sector = st.selectbox("Select a Sector", list(SECTORS.keys()))
    
    # Performance period selection
    period = st.selectbox("Select Performance Period", ['1mo', '3mo', '6mo', '1y'], index=0)
    
    if st.button("Get Top Performers"):
        if sector:
            with st.spinner(f'Fetching top performers for {sector} sector over {period}...'):
                top_stocks = get_high_performing_stocks(sector, top_n=5, period=period)
                if not top_stocks.empty:
                    st.subheader(f"Top 5 High Performing Stocks in {sector} Sector over {period}")
                    st.dataframe(top_stocks[['Ticker', 'Company', 'Performance (%)']])
    
                    # Bar Chart
                    fig = go.Figure(data=[
                        go.Bar(
                            x=top_stocks['Ticker'],
                            y=top_stocks['Performance (%)'],
                            marker_color=COLORS[:len(top_stocks)]
                        )
                    ])
                    fig.update_layout(title="Top Performing Stocks", xaxis_title="Ticker", yaxis_title="Performance (%)")
                    st.plotly_chart(fig)
                else:
                    st.warning("No high performing stocks found.")
    
    st.markdown("---")
    st.markdown("**Note:** This is a demonstration. In a production application, consider integrating a dynamic data source for sector stocks.")

if __name__ == "__main__":
        main()